A Quantitative 3D Subcellular Map of Human Retinal Pigment Epithelium. A substantial README file is available on the home page.

Supporting information for the NIST Internal Report entitled: Per- and Polyfluoroalkyl Substances - Non-Targeted Analysis Interlaboratory Study Final Report.

A zip file by the name ESD_surf_gradesc.zip which contains a
software package that can be used to compute the elastic shape registration of two simple
surfaces in 3-dimensional space and the elastic shape distance between them with an algorithm
based on gradient descent for reparametrizing one of the surfaces using as the input initial
solution to the algorithm the rotation and reparametrization computed with another algorithm
based on dynamic programming for reparametrizing one of the surfaces to obtain a partial elastic

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of malware packer classification AIs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for mitigating/removing that trigger behavior from the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of malware packer classification AIs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for mitigating/removing that trigger behavior from the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of malware packer classification AIs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for mitigating/removing that trigger behavior from the trained AI models.

CTenC is a Cartesian TENsor Calculus package for performing manipulations of tensor expressions using index notation in Mathematica. The package is designed and optimized for problems in engineering and material science, including solutions of field equations (Stokes, Laplace, etc.) and developing constitutive equations in tensor-based formalisms such as GENERIC. Functions are given for the manipulations of free and dummy indices.

This data set contains the spectral data associated with the collection of EC-SERS spectra using mainly a nontargeted drug identification approach, with several samples using a targeted fentanyl identification approach. The data set contains the replicate measurements and averaged Raman spectra used in the characterization of the analytes (drugs of abuse and adulterant compounds) to allow for forensic library formation. The data set also contains spectra of analytes collected at varying concentrations and additional fentanyl analog data collected using a targeted method.

This dataset consists of characterization of telechelic functional polyethylene oligomers, precursors, and resultant step-growth polyethylene with urethane-linkages between segments of different lengths. Polymer characterization data includes nuclear magnetic resonance (NMR) spectra, room-temperature and high-temperature size exclusion chromatography data and associated calibration curves, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra, transmission FTIR spectra with melting.

This document is part of a series of reports describing experimental property measurements completed at the National Institute for Petroleum and Energy Research (NIPER) in Bartlesville, Oklahoma, in the 1980s and 1990s. Members of the Bartlesville Thermodynamics Group included William D. "Bill" Good, William V. "Bill" Steele, Bruce E. Gammon, Norris K. Smith, Stephen E. Knipmeyer, An "Andy" Nguyen, Timothy D. Klots, I. A. "Alex" Hossenlopp, Aaron P. Rau, William B. Collier, John F. Messerly, Ann G. Osborn, Susan Lee Bechtold, Donald G. Archer, Ian R.